Ice melting and sea level rise

So far, we have talked about ice melting from sea ice, glaciers and ice sheets, and have mentioned some serious impacts. This time, we are going to systematically look at one significant global issue derived from ice melting, i.e. sea level rising. Will the world be submerged in the future?



Figure 1. Processes relevant to sea level change in IPCC. (Source: IPCC, 2013)

There are three factors contribute to sea level rise, including thermal expansion, added water from land ice (glaciers and ice sheets) and change in basin depth. Thermal expansion is the biggest contributor to sea level rise (IPCC, 2013), and the topographic change currently contribute to 3 mm/yr drop in sea level due to glacial isostatic adjustment. I will only discuss the contribution from ice melting below, which is sea level change from changes in mass of ocean. The unit is sea level equivalent (SLE), mentioned in previous posts, i.e. sea level equivalent to a mass of water (mass divided by density (1000kg/m³) and area (362500 billion m²) Approximately, adding 362.5 Gt water will lead to 1 mm rise in sea level. Figure 1 shows how IPCC AR5 assessed the processes involving in modelling sea level change, and the linkage between the relevant sections. Sea level change can be modelled via several types of model, while Atmosphere-Ocean General Circulation Models (AOGCMs) provide most comprehensive simulations. Regional Climate Models (RCMs) with information form AOGCMs are important to simulating changes in glaciers and ice sheets.

Ice losses from glaciers and ice sheets have been mentioned in early posts (the first and the last three posts) with unit in Gt. Convert them to SLE:

• Total ice loss from glaciers in SLE was 0.62 ± 0.37 mm/yr during 1971-2009, 0.76 ± 0.37 mm/yr during 1993-2007, and 0.83 ± 0.37 mm/yr during 2005-2009;
• During 1992-2001, ice loss was -0.02 to 0.20 mm/yr from Greenland ice sheet and -0.10 to 0.27 mm/yr form Antarctica ice sheet, and during 2002-2011 the rate was 0.43-0.76 mm/yr from Greenland and 0.20-0.61 mm/yr from Antarctica.

Figure 2 shows the contribution to sea level change up to 2100 under different scenarios. Major contribution would come from ice melting from glaciers, contributing to 0.08-0.38 m rise by the end of this century, while 0.10-0.19 m mean rise under 4 RCPs (IPCC, 2013) . Based on modelled future projections, Greenland ice sheet would contribute to a rise below 0.11 m, while Antarctica ice sheet would likely to drop sea level with predicted increase in precipitation, according to IPCC (2013).

Figure 2. Future projections of sea level rise under 4 RCPs. (Source: IPCC, 2013)

 



















 

Ice loss from ice sheets 2: Antarctica ice sheet

Hope you have enjoyed your Christmas holidays!


Following last post, we are going to look at Antarctica ice sheet.  About 90% of ice on the Earth, which is the largest potential contributor to sea level rise in the future. Antarctica ice sheet consists of three parts, West Antarctica, East Antarctica and the Antarctica Peninsula. Figure 1 show a schematic structure of Antarctica.


Observations and scientific understanding of Antarctica remain at low level (IPCC, 2013), which cause poor understanding of ice loss at Antarctica ice sheet. Overall, ice has kept losing over the last two decades though with large uncertainty, as showed in figure 2. The loss rate had increased from -135 to -58 Gt/yr during 1993-2010 to -221 to -74 Gt/yr during 2005-2010 (IPCC, 2013). The loss was caused by warming in temperature, as well as warming in tropical sea surface temperature (Ding et al., 2011). Shepherd et al. (2012) found that ice loss occurred at the Antarctica Peninsula and the West Antarctica with accelerating rate, while the East Antarctica had net gain ice. The three maps in lower Figure 3 show these changes in ice. 


Figure 1. Cross section of Antarctica ice sheet. (source: LIMA, NASA)



Figure 2. Cumulative ice loss from Antarctica ice sheet. (Source:IPCC, 2013)






Figure 3. Ice loss rate (Source: IPCC, 2013)


However, a relative new study by NASA indicated that Antarctica ice sheet gains more ice than losses. Though this study agreed with IPCC (2013) and Shepherd et al. (2012) that
ice loss occurred at the Antarctica Peninsula with accelerating rate, it showed that
East Antarctica and the interior of West Antarctica had net gain of ice with thicker at the rate of 1.7 cm/yr, compensating ice discharge over the whole ice sheet and even reducing sea level rise at the rate of -0.23 mm/yr. This challenges the statement that Antarctica ice sheet currently contributes to sea level rise at the rate of 0.27 ±0.11 mm/yr over 1993-2010 and 0.41 ±0.20 mm/yr over 2005-2010 (IPCC, 2013). Future studies are required here in order to solve the accurate contribution of Antarctica ice sheet on sea level rise.

Figure 4. Changes in ice by NASA's study.




Ice loss from ice sheets 1: Greenland ice sheet

A ice sheets is a enormous glaciers that cover an area over 50,000 km². Two.ice sheets are Greenland ice sheet (7 × 10⁶ km²⁾ and Antarctica ice ice sheet (1.4 × 10⁷ km²). They, together,  contain about 99% freshwater ice over the world. 2/3 of sea level rise is contributed by ice melting from the two sheets (IPCC, 2013), the major source of freshwater to the ocean. Figure 1 shows how the two ice sheets form and flow.

Figure 1. Form and flow of Greenland and Antarctica ice sheets. (Source: adopted from  LIMA, NASA)

Greenland

Change in mass of ice sheets is the net mass gain over precipitation and melt and runoff, and it can be measured by mass budget, repeated altimetry and temporal variations in Earth gravity field (IPCC, 2013). Over the last few decades, Ice mass loss has occurred in Greenland with decline rate accelerated: the average loss rate had accelerated from -121 Gt/yr over 1993-2010 to -229 Gt/yr over 2005-2010 (IPCC, 2013). Up till now, the highest ice loss occurred in 2012, as simulated by model MARv3.5.2 (Figure 2). In this year (2016), the loss is estimated about -144 Gt/yr extra with respect to 1981-2010 average loss, according to the model. The melt season occurred earlier than previous years. 

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Figure 2. Anomalies of surface mass balance, snowfall and runoff form the Greenland ice sheet, simulated by model MARv3.5.2.(Source: adopted from Laboratoire de Climatologie et Topoclimatologie).

Fettweis et al. (2012) used the MAR model to estimate the contribution of surface mass balance from the Greenland to global sea level rise in the future. The model is a regional climate model (RCM) forced by general circulation models (GCMs) from CMIP5 and ICE2SEA under scenario RCP4.5 and RCP8.0. Their study showed that models used in the study simulated continuous ice loss from Greenland within this century (Figure 3), positive correlated to temperature rise but with non-linear relationship (Figure 4). 

Figure 3. Annual changes in surface mass balance, snowfall, rainfall, runoff and contributions to sea level rise. (Source: adopted from Fettweis et al. (2012))

Figure 4. Annual changes under RCP4.5 and RCP8.0. (Source: adopted from Fettweis et al. (2012))

 

 

Melting glaciers

Last time, we looked at two major issues from glacier melting at Himalayas. Himalayan glaciers are important, because they are sources to rivers and ‘water tower’ of Asia to support Asian population. However, these glaciers only take small proportion of glaciers worldwide. This time, we will look at the fate of glaciers over the world. Though difficult to estimate glaciers due to uncertainties in measurement, a global glacier mass is likely in the range of 114,000-192,000 Gt (IPCC, 2013).

Figure 1. Global distribution of glaciers, based on RGI regions. (Source: adopted from IPCC, 2013).

Figure 1 shows the global distribution of glaciers, divided into 19 Randolph Glacier Inventory (RGI) regions. Large proportion of glaciers are distributed in high-latitude regions, which are dominant sources contributing to sea level rise caused by glacier melting. Changes in glaciers can be measured via mass, volume, length and area. Table 1 shows these methods and their characteristics.

                             Table 1. Measurement methods, adopted from IPCC (2013)

Glaciers are sensitive to climate change (temperature and precipitation in particular), and they adjust to new equilibrium to balance the change. Over the last few decades, global glaciers have retreated overall with considerable mass loss in order to balance global warming, e.g. all RGI regions lost glacier mass during 2003-2009 with rate at –259 ± 28 Gt/yr in total (Gardner et al., 2012). Figure 2 shows the glacier mass budget at RGI regions during the period. Large mass loss occurred in Alaska (–50 ± 17 Gt/yr), Greenland periphery (–38 ± 7 Gt/yr), Arctic Canada North (–33 ± 4 Gt/yr), Southern Andes (–29 ± 10 Gt/yr), Arctic Canada North(–27 ± 4 Gt/yr) and High-Mountain Asia (–26 ± 12 Gt/yr) (Gardner et al., 2012). 

Figure 2. Global mass budget in Gt/yr, based on GRI regions. (Source: adopted from Gardner et al., 2012)

Marzeion et al. (2012) used CRU-forced model to simulate glacier mass change 1901-2010 (Figure 3), using unit SLE. SLE represents sea level equivalent to glacier mass. Their simulations showed that glacier mass gradually lost during the period, with peak loss rate occurred in 1930s caused by glacier melting at Greenland. Peak loss rate of glacier mass at Russian Arctic occurred between 1950 and 1960, as well as Arctic Canada. After low loss rate in 1970s, the rate increased again.

Figure 3. Cumulative global surface mass balances relative to the 1986–2005 mean, and rates from the CRU-forced model. (Source: adopted from Marzeion et al., 2012)

Future projection (Marzeion et al., 2012) shows gradual loss in glacier mass in all RCPs towards 2100 (Figure 4), with rate increasing until the middle of this century . After mid-century, the rates under RCP2.6 and RCP4.5 will slow down, while the rates under RCP6.0 and RCP8.5 will still increase, according to Figure 4.

Figure 4. Cumulative global surface mass balances and rates from the model forced with CMIP5 projections under 4 RCPs (RCP2.6 in red, RCP4.5 in green, RCP6.0 in blue and RCP8.5 in pink) toward the end of this century. Solid lines represent the mean of simulations from individual models in CMIP5. (Source: adopted from Marzeion et al., 2012)

 There are about 170,000 glaciers, and they have their own characteristics and climate conditions, leading to their different response to climate change with various time scales. For example, some glaciers at Alaska have retreated quickly (Gardner et al., 2012), while some at Karakoram mountain range have been stable or even have advanced (Bolch et al., 2012) Meanwhile, many glaciers are still poorly known. These result in considerable uncertainties in future projections, particularly in mountain regions like the Karakoram-Himalaya mountain range with complex glacier properties and climate conditions (IPCC, 2013).

Melting Himalayas: Flood and freshwater shortage

Though melting at Arctic sea is a serious issue, melting at Himalayas is more concerned by public. Himalayan mountains contain 40% of freshwater over the world and thousands of glaciers, and they are the source of the seven greatest rivers in Asia. 20% of the world’s population rely on Himalayan glaciers as water sources. Large proportion of glaciers here are fed by snowfall from summer monsoon, however, global warming reduces precipitation from monsoon and extends melting period (Bolch et al., 2012). This leads to many serious problems. There is a nice video called “Himalayan Meltdown” introducing these problems. I just put its trailer here for you to get a feel for how serious the melting at Himalayas is.

One major issue threatening Asia is glacier lakes. Melted water forms thousands of glaciers lakes in the region, and water in glacier lakes can accelerate melting rate via transmit heat efficiently to glacier in contact with glacier ice (Bolch et al., 2012). Meanwhile, many of these lakes are likely to burst at the seams. For example, Glacial Lake Outburst Floods (GLOFs) Assessment (2010) identified  more than 200 lakes in the region likely to burst. When they burst, villages at downstream are threatened and may destroyed  by floods. Governments put a lot of effort into reduce the water level of glacier lakes. Bhutan Government had successfully reduced water level of Lake Thorthormi. However, this is not a permanent solution as long as glaciers keep melting and feed the lake. 

Another problem is drought. Since global warming reduces precipitation from seasonal monsoon, the proportion of runoff based on glacier melted water increase (Bolch et al., 2012). When glacier dries up, there will be serious issues in water supply. According to the video, some villages have faced water shortage. Downstream discharge is significantly affected by upstream discharge. Immerzeel et al. (2010) used A1B SRES scenario to simulated mean upstream discharge (Figure 1), and found decreases in major Asian rivers. They concluded that 4.5% of the total population will have food security problem due to decreasing water supply.

Figure 1. Simulated mean upstream discharge.

Ice melting at Arctic Sea 4: effects, bad or good?

Continuing the previous series, this will be the last post about sea ice melting. From the early posts, both observations and simulations show that sea ice at Arctic sea has declined significantly with melting rate accelerated. Sea ice would be likely to decrease continuously within this century, and might reach nearly ice-free status. So, what are the effects?

 Climate 

Arctic sea ice affects global climate change through complex processes. Firstly, there is positive feedback between warming and ice melting (as showed in Figure 1, Dessler, 2011). Current global warming has caused and will cause more sea ice melting at Arctic sea. Decreasing ice cover while increasing sea surface leads to the decrease in albedo, as sea surface reflects less and absorbs more solar radiations than ice surface. More absorbed energy leads to extra warming, thus, melting more ice. Therefore, ice melting at Arctic sea will amplify the global warming in this century. 

Meanwhile, sea ice also insulates the below sea water from the atmosphere, preventing thermal exchange and gas exchange between the ocean and the atmosphere. Sea ice loss at Arctic sea changes the interaction between the ocean and the atmosphere. Melting water decreases the salinity of sea water as well, which changes the density of sea water affecting ocean circulation. This will affects the climate at global scale.

Figure 1. Ice-albedo feedback

 Ecosystem

Sea ice is the habitat to many species, from fungus to large mammals like polar bears. However, sea ice loss has threatened their survival, affecting the population of the species. The US Geological Survey studied how the Pacific walrus and polar bears response to the rapid decrease of sea ice at Arctic sea. They found that stampeding events (Oakley et al., 2012) could be to blame for the high mortality in young walruses, as walruses had to crowd living on the shores of Alaska and Russia with less sea ice available. They also found that longer swimming distance could be blame for the decrease in the survival of polar bears (Oakley et al., 2012), with less sea ice available as well. Loss of hunting habitats could also cause the decline in polar bear's population (IPCC WG2, 2014). An earlier study (Stirling and Parkinson, 2006) found that the decline could be substantially. 

Source: USGS

Sea ice loss has also challenged the local biodiversity. One problem is hybrid. A hybrid of a grizzly and a polar bear was hunted in the Arctic in 2006 (Kelly et al., 2010). The explanation could be that polar bears live in the same area as grizzlies because of the loss in their habitats, sea ice.


 Human activities

Though ice melting leads to many bad impacts, there is a good news for human. Nearly ice-free status can provide open Arctic ocean. Ships may be able to navigate through Arctic ocean within this century, saving lots of time. Melia et al. (2016) simulated routes through Arctic, and concluded that shipping from Europe to Asia through the Arctic could become more than 10 days faster, and could save 4 days from N. America to Asia.

Ice melting and the United Nations

Earlier this month (7-18th), COP 22, CMP 12 and CMA 1 to the UNFCCC were held in Bab Ighli, Marrakech, Morocco. Taking this opportunity, I really want to share the two major works relevant for ice melting done by the United Nations (UN) last year, since I had an internship at United Nations Environment Programme (UNEP) China office this summer.

Sustainable Development Goals (SDGs)

At the end of September, 2015, 17 Sustainable Development Goals (SDGs) were adopted during General Assembly of the United Nations, in which the Goal 13 was defined as “Goal 13. Take urgent action to combat climate change and its impacts*”. 5 targets were set up in order to achieve the goal. Mitigating ice melting was a key motivation to set up this goal, as sea ice extent at Arctic has decreased over the last several decades, as well as ice will continually melt within this century as a result of global warming.

 Paris Agreement

Paris Agreement aims to arrange how to combat climate change globally after 2020. The agreement was adopted on COP21 in Paris on 12 December 2015, and was opened to sign on 22 April 2016 in New York. 16 days ago (4th Nov. 2016), it went to effect. Up till writing this sentence, 122 parties have ratified this agreement, and more parties will ratify sooner. This agreement is a milestone in combating global warming globally. I think it has been the most efficient action so far against climate change, because it clarifies the obligatory goals all countries should work together to achieve. This time, countries are going to be responsible for global benefits, instead of only considering their owns.

 One obligatory goal is to control the global average temperature warming to well below 2 °C, and to pursue efforts to hold the increase in temperature to to 1.5 °C, with both compared with pre-industrial levels. Ice melting would be controlled, if this goal could be achieved by the end of this century.

Sea ice melting at Arctic Sea 3: Evaluation -- can we trust the future projections?

Whether we can trust the projections is based on how well the projections can match observations and represent what will likely happen in the future. Therefore, the models need to be evaluated, via comparing model simulations with observations. Model evaluation is a key component in my academic background (Environmental Modelling), and I may write some knowledge beyond those related to sea ice below. To begin with, I adapt two figures to demonstrate the quality of model simulations. 

Figure 1. Comparison between observed seasonal cycle and modelled seasonal cycle. (source: Stroeve et al., 2012). 

The first one (Figure 1) shows seasonal cycle of sea ice extent at Arctic sea from model simulations and observations 1979-2011 (Stroeve et al., 2012). The multi-model means, particularly the CMIP5’s (diamonds), quite match the observations (red line). All the diamonds are placed between the maximum and minimum observations of each month. IPCC (2013) stated that the error in multi-model mean is less than 10% of the observations. The quality of model simulations have been improved. 

Figure 2. Modelled and observed sea ice extent 1900-2012. Each colour line represents a singe simulation from an individual model. Black lines represent observations. Red line shows multi-model mean from CMIP5, and blue line shows that from CMIP3.  Red shade shows simulations range from CMIP5, and blue shade shows that from CMIP3. (source: IPCC,2013).

From Figure 2, it is obvious that the model simulations from CMIP5 is better than those from CMIP3’s, because the multi-model mean of September ice extent from CMIP5 better matches the value of observations as well as catches up the sharp decline in the last few decades. The improvements were achieved by improving parameters used in modelling sea ice and improvements in other environmental components affecting sea ice. For instance, according to Stroeve et al (2012), one contributor of improvement from CMIP3 to CMIP5 is the improvement in parameters of sea ice albedo. The improvement can also be contributed to the improvement in simulating atmosphere (Notz et al., 2013), as sea ice is formed by the interactions between the atmosphere and ocean.

With the comparison between model simulations and observations, can we now trust the projections? We still need to consider uncertainties. There are three major types of uncertainty, which are internal variability, model uncertainty and scenario uncertainty. Their relative proportions are various with different spatial and temporal resolution.Natural fluctuations, coming from when any radiative forcing is absent, causes internal variability. The internal variability is a key reason for Notz et al. (2013) to against comparing model simulations with the observations directly, as their study found that the internal variability can cause a range of trends in model realisation. Model uncertainty is the difference among the different models’ simulations when responding to the same radiative forcing. This is why models in CMIP5 provide different simulations (covering a wide range) in sea ice extent and its relationship with annual global surface warming (for details, see last post). The last one, scenario uncertainty, is the difference in predicting greenhouse gases emissions in the future, and this be used to explain why Arctic sea would reach nearly ice-free at different time under different RCPs. I found a nice figure to show the relationship among the uncertainties, but it is not about sea ice (Figure 3).

Figure 3. Relative relationship among the three types of uncertainty. (source: Hawkins and Sutton, 2009).

Sea ice melting at Arctic Sea 2: Future projections

Last time, we looked at the present decline in Arctic sea ice. The decline rate has become faster and faster, and this is a very serious issue in climate change. It is necessary to find out how sea ice would change in the future. This is dominantly relied on future projections, which are based on model simulations under different scenarios. This time, we are going to look at model simulations and future projections about Arctic sea ice. 

At the beginning, let’s clarify some definitions and knowledge that’ll  help you to understand what I am going to write below. If you've already known these, please skip this part. (Since I'm studying modelling now and my undergraduate dissertation was related to modelling and projections, I list some extra academic journal articles here which I think are worth reading if you are interested in.)

• What is a model? And what’s a climate model?

 In one sentence, a model is a representation of the reality, helping to predict or understand something. It is a series of rules and principles to convert inputs (the data we have now) to outputs (what we want). 

A climate model uses quantitative methods to investigate how the climate system responses to variety of forcing, to predict how the future climate would be with different time scales and to make projections of future climate towards 2100 and beyond.

• What are CMIP3 and CMIP5?

CMIP3 and CMIP5 are the two phases of the Coupled Model Intercomparison Project (CMIP) for evaluation in Assessment Report  by Intergovernmental Panel on Climate Change (IPCC). IPCC reports summarise most advanced outstanding studies and researches in the world related to climate change. 

I think you just need to know that CMIP contains a series of climate models that simulate past and future climate, and the future projections are based on multi-model mean and variations among model simulations. That’s enough to understand what I am going to write below. If you want to know more about it, there is an excellent journal article providing the scientific explanation of CMIP5, i.e. Taylor et al. (2012).

•  What are RCPs?

RCP is the Representative Concentration Pathway, and there are four types of it : RCP2.6, RCP4.5, RCP6.0 and RCP8.5. You can simply think that the numbers at the end represent how much the radiative forcing would increase by the end of this century in W/m². e.g. in RCP8.5, radiative forcing reaches 8.5  W/m² by 2100, have highest temperature warming among the four. If you are interested in this, you can look at van Vuuren et al. (2011) for further information.

Okay, now let's go back to today’s topic. CMIP5 models under RCPs provide most comprehensive and advanced projections in the world, and my evidence and examples below are derived from CMIP5 model simulations in IPCC AR5 Chapter 12. The projections can be divided into near-term and long-term time scales. The near-term covers time from now to the mid of this century. IPCC (2013) stated that the near-term projections are not specific and precise enough as the result of changes in external forcing, so I’m not going to look at the near-term projections here.

I’m going to focus on the long-term (towards the end of this century) projections instead. Firstly, we have to determine the basis of model simalations. Sea ice melting in the future is very likely caused dominantly by further rise in surface temperature (IPCC, 2013). Figure 1 shows the relationship between decline in sea ice extent and annual global surface warming in CMIP5. Within the figure, it is obvious that there is a functional relationship (nearly linear) between sea ice extent decline and surface temperature rise before reaching nearly ice-free status (the black horizontal line at 1 × 106 km2  ). Nearly ice-free is that sea ice extent is continuously less than 1 × 106 km2  for no less than 5 years. 

Figure 1. Relationship between annual mean global surface warming and September Arctic sea ice extent relative to the period 1986-2005 as simulated by CMIP5 models. (Source: IPCC, 2013)

Then, let’s look at the future projections. Based on CMIP5, the decline in sea ice shows in all four RCPs but with different rates. According to Figure 2, a general decline in sea ice extent is simulated under each RCP, both for winter (Feb.) and summer (Sep.). I’m more interested in the changes in September and will focus on this in the following sentences, because the condition in September may change to nearly ice-free within this century. I’ve added a green horizontal line in the September one to show the nearly ice-free status. All four RCPs show the possibility of the nearly ice-free before 2100. Under RCP8.5, some model simulations show that the Arctic sea may reach nearly ice-free before 2040. By the end of this century, nearly all models (around 90% according to IPCC) under RCP8.5 reach the nearly ice-free status, while that is about half under RCP2.6 (around 45% according to IPCC). This means that if climate change keeps happening in the future, sea ice in Arctic sea will keep melting and the sea will become nearly ice-free within few decades. 

Figure 2. Changes in ice extent for period 1950-2100 as simulated by CMIP5 models under different scenarios. (Source: IPCC, 2013).